A Novel Technique for Grading of Dates using Shape and Texture Features
This work addresses a domain-specific problem for agricultural quality control, but it is incremental as it applies existing techniques to a new dataset.
The paper tackles the problem of grading date fruits by developing a method that combines shape and texture features, achieving the highest accuracy among tested classifiers.
This paper presents a novel method to grade the date fruits based on the combination of shape and texture features. The method begins with reducing the specular reflection and small noise using a bilateral filter. Threshold based segmentation is performed for background removal and fruit part selection from the given image. Shape features is extracted using the contour of the date fruit and texture features are extracted using Curvelet transform and Local Binary Pattern (LBP) from the selected date fruit region. Finally, combinations of shape and texture features are fused to grade the dates into six grades. k-Nearest Neighbour(k-NN) classifier yields the best grading rate compared to other two classifiers such as Support Vector Machine (SVM) and Linear Discriminant(LDA) classifiers. The experiment result shows that our technique achieves highest accuracy.